Detecting　and　repairing　road　damage　timely　is　crucial　for　ensuring　traffic　safety　and　reducing　hazards.　Cracks　and　potholes　are　the　primary　indications　of　early-stage　road　damage.　However,　existing　deep　learning-based　methods　for　road　damage　detection　often　have　limited　feature　extraction　capabilities　and　only　perform　well　in　specific　detection　environments.　To　address　these　challenges,　this　paper　proposes　an　improved　UNet-based　road　damage　segmentation　method　for　complex　environments.　This　method　incorporates　Atrous　Spatial　Pyramid　Pooling　(ASPP)　and　Coordinate　Attention　(CA)　into　the　network,　enhancing　its　ability　to　capture　features　of　various　sizes　and　to　localize　feature　information.　The　proposed　model　effectively　detects　both　cracks　and　potholes　under　different　road　environments,　such　as　concrete,　asphalt,　and　gravel.　Experimental　results　demonstrate　that　the　proposed　network　achieves　78.8%　and　88.57%　segmentation　Intersection　over　Union　(IoU)　for　cracks　and　potholes,　respectively,　outperforming　classical　semantic　segmentation　networks　such　as　UNet,　PSPNet,　Attention　UNet,　UNet++,　DANet,　SegFormer,　and　TransUNet.　This　model　can　be　applied　to　intelligent　road　inspection　and　maintenance　to　improve　inspection　efficiency.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2023.